Cargando…

A novel framework for classification of selection processes in epidemiological research

BACKGROUND: Selection and selection bias are terms that lack consistent definitions and have varying meaning and usage across disciplines. There is also confusion in current definitions between underlying mechanisms that lead to selection and their consequences. Consequences of selection on study va...

Descripción completa

Detalles Bibliográficos
Autores principales: Björk, Jonas, Nilsson, Anton, Bonander, Carl, Strömberg, Ulf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294651/
https://www.ncbi.nlm.nih.gov/pubmed/32536343
http://dx.doi.org/10.1186/s12874-020-01015-w
Descripción
Sumario:BACKGROUND: Selection and selection bias are terms that lack consistent definitions and have varying meaning and usage across disciplines. There is also confusion in current definitions between underlying mechanisms that lead to selection and their consequences. Consequences of selection on study validity must be judged on a case-by-case basis depending on research question, study design and analytical decisions. The overall aim of the study was to develop a simple but general framework for classifying various types of selection processes of relevance for epidemiological research. METHODS: Several original articles from the epidemiological literature and from related areas of observational research were reviewed in search of examples of selection processes, used terminology and description of the underlying mechanisms. RESULTS: We classified the identified selection processes in three dimensions: i) selection level (selection at the population level vs. study-specific selection), ii) type of mechanism (selection in exposure vs. selection in population composition), iii) timing of the selection (at exposure entry, during exposure/follow-up or post-outcome). CONCLUSIONS: Increased understanding of when, how, and why selection occur is an important step towards improved validity of epidemiological research.